DaRUS (University of Stuttgart)
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Ikarus v0.4
No full textIkarus is a C++-based library with Python bindings (link to documentation) built to solve partial differential equations with the finite element method. The dataset under this DOI contains the current release (v0.4) of the library.
This release not only focuses on refactoring various interfaces but also introduces exciting features such as Python bindings, result evaluators, the Kirchhoff-Love shell element, added support for Clang 16, and more. For more details on the changes of this release refer to the repo file CHANGELOG.md. or go to GitHub.
This dataset includes the Ikarus source code itself as a zip-file (ikarus-v0.4.zip).
Additionally, this dataset comes with a Docker container (ikarus-docker-v0.4.tar.gz). It has Ikarus itself for this particular release installed, and can be used, for instance, to execute examples. For more details on the examples, see here.
This version's documentation (ikarus-docs-v0.4.zip) is also included, and it can be accessed by navigating to the top-level file "index.html" in a browser.
The download instructions can be found here
Code and benchmark for NPCS, a Native Provenance Computation for SPARQL
No full textCode for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL.
The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenance polynomials (i.e., how-provenance data). The translation rules implemented for the query rewriting can be seen in the paper.
Also, the code contains scripts that include scripts and services to automatize the query execution.
We use GraphDB (version 10.2.0) and Stardog (version 9.1.0) for the SPARQL endpoints. Because of the license restrictions, these software products cannot be included in this dataset and must be downloaded from the respective vendors. Also, the data must be loaded using the respective bulk loaders of GraphDB and Stardog.
The datasets used in the experiments can be generated synthetic dataset generator of the WatDiv benchmark. The Wikidata dataset corresponds to the full RDF dump from May 22, 2023.
Do not hesitate to contact the authors for any inquiries.</p
Replication Data of Kästner group for: "Chirality Transfer of Stereogenic Boron Centers Enabled by a SN2-Type Mechanism"
No full textIn this dataset, all optimized geometries from the calculations are listed. The files are given in xyz-format
Replication Code for: Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems
No full textThis dataset includes the code to reproduce the results from the paper titled "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems". In this paper error bounds for randomized symplectic basis generation techniques are proven. The numerical experiments where error decay rates and runtimes from the randomized methods and the classical, non-randomized methods are compared can be reproduced using this code. See the README for more information and installation instructions
Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis - Replication data
No full textThis dataset contains the supplementary materials to our publication "Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis", where we report on a study we conducted. Please refer to publication for more details, also the abstract can be found at the end of this description. The dataset contains:
The collection of graphs with layout used in the study
The final, randomized experiment files used in the study
The source code of the study prototype
The collected, anonymized data in tabular form
The code for the statistical analysis
The Supplemental Materials PDF
The documents used in the study procedure (English, Italian, German)
Paper abstract:
Problem solving is a composite cognitive process, invoking a number of cognitive mechanisms, such as perception and memory.
Individuals may form collectives to solve a given problem together in collaboration, especially when complexity is perceived to be high.
To determine if and when collaborative problem solving is desired in the context of visual graph analysis, we compare ad hoc pairs to individuals and nominal pairs, when solving different tasks in mixed reality.
We discuss the results of an experiment with 72 participants performed in two countries and three languages.
We apply the concept of task instance complexity to quantify the visual demand of tasks used in the experiment.
Our results show the importance of using nominal groups as a benchmark for evaluating collaborative virtual environments.
We conclude that 3D graph representation is not sufficient to induce better collaborative results compared~to the benchmark
Systemmodell eines Mobilitätssystems
No full textDieses Repository enthält die Beschreibung des System-Modells im Minimalbeispiel aus Kapitel 4 der Dissertation in textueller Notation. Die jeweiligen Dateien sind wie folgt strukturiert:
Kontext-Analyse des Minimalbeispiels
01_ConstituentSystemView
01_MobilitySystemView
01_SystemActorView
01_SystemEvolutionView
01_SystemMissionsView
01_SystemRelationsView
Anforderungs-Analyse des Minimalbeispiels
02_EmergentRequirementsView
02_MobilitySystemRequirementsView
02_RequirementsTracingView
02_StakeholderNeedsView
02_StakeholderUseCasesView
02_UseCaseSpecificationView
Funktionale Analyse des Minimalbeispiels
03_FunctionalEvaluationView
03_InternalSystemsFunctionView
03_MobilityConceptsView
03_MobilityOperationsView
03_MobilitySystemFunctionsView
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Replication Data for: A HPX Communication Benchmark: Distributed FFT using Collectives
No full textThis repository complements the identically titled poster paper submitted to Euro-Par 2024 and allows to reproduce the published results. For a description on how to use the code please consider the README file
Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten
No full textThe dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten":
DFT input files: INCAR, KPOINTS.
All POSCAR files for DFT and thermodynamic integration
Moment tensor potential (MTP) file
Training dataset for MTP
All Hessian Matrix files for thermodynamic integration. For the transition state, the stabilized Hessian Matrix is provided.
All imaginary mode files for transition state
Lattice expansion used in all calculations.
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Datensätze und Modelle zur Dissertation Kamerabasierte Topologieschätzung zur roboterbasierten Handhabung von verzweigten Leitungssätzen
No full textDatensätze und Modelle zur Dissertation Kamerabasierte Topologieschätzung zur roboterbasierten Handhabung von verzweigten Leitungssätzen. In den Daten sind Rohdaten mit Annotierung, teilweise 2D+Tiefendaten, Kalibrierungsdaten und verarbeitetete Rohdaten inkl. Modelle zur Verarbeitung zu finden.
Die Daten sind zum Teil am Prüfstand mit Raspberri Pi Kameras, mit der Nerian Scarlet Stereokamera und mit Intel RealSense-D435i entstanden. Modellgewichte sind von PyTorch. Annotierungen sind als .json Format normalisierte Splinepunkte eines Leitungssatzes. Daten + Modelle können für Reproduzierbarkeitszwecke, ergänzende Prädiktionen zur Dissertation oder als Benchmark für neue Modelle zur Topologieschätzung genutzt werden
Lidar_StreamlineXR_Starring_Winset_Test_Site
No full textComplementary lidar measurements to the ANDroMeDA-UAV measurements in the area of the Winsent test field. The Streamline XR lidar system was operated in starring mode with gate overlapping, which enables a spatial resolution of 1.5m along the laser beam. The file name contains the start time (UTC) of the trajectory. Each file contains 100 time (rows) steps over 1460 measurement distances (45m to 2233.5m; columns